8,938 research outputs found
Network-Level Performance Evaluation of a Two-Relay Cooperative Random Access Wireless System
In wireless networks relay nodes can be used to assist the users'
transmissions to reach their destination. Work on relay cooperation, from a
physical layer perspective, has up to now yielded well-known results. This
paper takes a different stance focusing on network-level cooperation. Extending
previous results for a single relay, we investigate here the benefits from the
deployment of a second one. We assume that the two relays do not generate
packets of their own and the system employs random access to the medium; we
further consider slotted time and that the users have saturated queues. We
obtain analytical expressions for the arrival and service rates of the queues
of the two relays and the stability conditions. We investigate a model of the
system, in which the users are divided into clusters, each being served by one
relay, and show its advantages in terms of aggregate and throughput per user.
We quantify the above, analytically for the case of the collision channel and
through simulations for the case of Multi-Packet Reception (MPR), and we
provide insight on when the deployment of a second relay in the system can
yield significant advantages.Comment: Submitted for journal publicatio
Distributed two-time-scale methods over clustered networks
In this paper, we consider consensus problems over a network of nodes, where
the network is divided into a number of clusters. We are interested in the case
where the communication topology within each cluster is dense as compared to
the sparse communication across the clusters. Moreover, each cluster has one
leader which can communicate with other leaders in different clusters. The goal
of the nodes is to agree at some common value under the presence of
communication delays across the clusters.
Our main contribution is to propose a novel distributed two-time-scale
consensus algorithm, which pertains to the separation in network topology of
clustered networks. In particular, one scale is to model the dynamic of the
agents in each cluster, which is much faster (due to the dense communication)
than the scale describing the slowly aggregated evolution between the clusters
(due to the sparse communication). We prove the convergence of the proposed
method in the presence of uniform, but possibly arbitrarily large,
communication delays between the leaders. In addition, we provided an explicit
formula for the convergence rate of such algorithm, which characterizes the
impact of delays and the network topology. Our results shows that after a
transient time characterized by the topology of each cluster, the convergence
of the two-time-scale consensus method only depends on the connectivity of the
leaders. Finally, we validate our theoretical results by a number of numerical
simulations on different clustered networks
Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks
Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network
Time scale modeling for consensus in sparse directed networks with time-varying topologies
The paper considers the consensus problem in large networks represented by
time-varying directed graphs. A practical way of dealing with large-scale
networks is to reduce their dimension by collapsing the states of nodes
belonging to densely and intensively connected clusters into aggregate
variables. It will be shown that under suitable conditions, the states of the
agents in each cluster converge fast toward a local agreement. Local agreements
correspond to aggregate variables which slowly converge to consensus. Existing
results concerning the time-scale separation in large networks focus on fixed
and undirected graphs. The aim of this work is to extend these results to the
more general case of time-varying directed topologies. It is noteworthy that in
the fixed and undirected graph case the average of the states in each cluster
is time-invariant when neglecting the interactions between clusters. Therefore,
they are good candidates for the aggregate variables. This is no longer
possible here. Instead, we find suitable time-varying weights to compute the
aggregate variables as time-invariant weighted averages of the states in each
cluster. This allows to deal with the more challenging time-varying directed
graph case. We end up with a singularly perturbed system which is analyzed by
using the tools of two time-scales averaging which seem appropriate to this
system
Coherence Potentials: Loss-Less, All-or-None Network Events in the Cortex
Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks
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